data_c = read_csv(here::here('./data/data_c.csv'), show_col_types = FALSE)
glimpse(data_c)
## Rows: 11
## Columns: 8
## $ MIN <dbl> 31.358, 26.333, 27.691, 17.322, 27.124, 32.055, 30.236, 32.634…
## $ FG_PCT <dbl> 0.572, 0.622, 0.453, 0.440, 0.510, 0.486, 0.535, 0.511, 0.544,…
## $ BLK <dbl> 1.147, 1.413, 1.618, 0.411, 0.804, 1.835, 0.784, 0.940, 1.329,…
## $ PF <dbl> 2.991, 2.307, 4.029, 2.889, 3.175, 3.307, 2.878, 2.888, 3.207,…
## $ OREB <dbl> 3.394, 2.713, 1.059, 0.922, 2.794, 2.386, 2.750, 3.418, 3.256,…
## $ REB <dbl> 10.688, 8.953, 4.765, 4.056, 8.742, 12.291, 10.277, 11.515, 12…
## $ Jogador <chr> "Deandre Ayton", "Jarrett Allen", "Jaren Jackson Jr.", "Moritz…
## $ Rookie <chr> "Novato", "Novato", "Novato", "Novato", "Novato", "Veterano", …
# Possível visualização sem zscore
test = data_c %>%
rowwise() %>%
mutate(across(FG_PCT:REB, ~ . / MIN))
data_c_sd = data_c %>%
mutate(sdFG_PCT = sd(data_c$FG_PCT)) %>%
mutate(sdBLK = sd(data_c$BLK)) %>%
mutate(sdPF = sd(data_c$PF)) %>%
mutate(sdOREB = sd(data_c$OREB)) %>%
mutate(sdREB = sd(data_c$REB)) %>%
mutate(mFG_PCT = mean(data_c$FG_PCT)) %>%
mutate(mBLK = mean(data_c$BLK)) %>%
mutate(mPF = mean(data_c$PF)) %>%
mutate(mOREB = mean(data_c$OREB)) %>%
mutate(mREB = mean(data_c$REB))
zscore_c = data_c_sd %>%
rowwise() %>%
mutate(zFG_PCT = (FG_PCT - mFG_PCT)/sdFG_PCT) %>%
mutate(zBLK = (BLK - mBLK)/sdBLK) %>%
mutate(zPF = (PF - mPF)/sdPF) %>%
mutate(zOREB = (OREB - mOREB)/sdOREB) %>%
mutate(zREB = (REB - mREB)/sdREB) %>%
select(zFG_PCT, zBLK, zOREB, zREB, zPF, Jogador, Rookie) %>%
rename(FG_PCT=zFG_PCT, BLK=zBLK, OREB=zOREB, REB=zREB, PF=zPF)
vis_c = zscore_c %>%
pivot_longer(., cols = c(FG_PCT, BLK, OREB, REB, PF), names_to = 'Var', values_to = 'stats') %>%
ggplot(aes(x = Var, y = stats, color = Rookie, label = Jogador)) +
geom_quasirandom() +
labs(
x = "",
y = "",
title = "Pivô",
colour = "Experiência"
)
ggplotly(vis_c, tooltip = "label")
data_pf = read_csv(here::here('./data/data_pf.csv'), show_col_types = FALSE)
glimpse(data_pf)
## Rows: 19
## Columns: 7
## $ MIN <dbl> 31.108, 25.786, 31.265, 29.267, 23.949, 31.439, 12.556, 31.682…
## $ FG_PCT <dbl> 0.423, 0.493, 0.568, 0.437, 0.514, 0.466, 0.274, 0.616, 0.491,…
## $ FTA <dbl> 3.412, 2.946, 4.078, 3.092, 1.915, 2.825, 0.556, 8.318, 2.350,…
## $ PTS <dbl> 16.775, 14.161, 20.324, 15.954, 10.339, 13.842, 2.978, 25.729,…
## $ REB <dbl> 7.676, 7.411, 9.912, 5.000, 5.559, 5.474, 2.711, 6.953, 3.200,…
## $ Jogador <chr> "Lauri Markkanen", "Marvin Bagley III", "John Collins", "Kyle …
## $ Rookie <chr> "Novato", "Novato", "Novato", "Novato", "Novato", "Novato", "N…
data_pf_sd = data_pf %>%
mutate(sdFG_PCT = sd(data_pf$FG_PCT)) %>%
mutate(sdFTA = sd(data_pf$FTA)) %>%
mutate(sdPTS = sd(data_pf$PTS)) %>%
mutate(sdREB = sd(data_pf$REB)) %>%
mutate(mFG_PCT = mean(data_pf$FG_PCT)) %>%
mutate(mFTA = mean(data_pf$FTA)) %>%
mutate(mPTS = mean(data_pf$PTS)) %>%
mutate(mREB = mean(data_pf$REB))
zscore_pf = data_pf_sd %>%
rowwise() %>%
mutate(zFG_PCT = (FG_PCT - mFG_PCT)/sdFG_PCT) %>%
mutate(zFTA = (FTA - mFTA)/sdFTA) %>%
mutate(zPTS = (PTS - mPTS)/sdPTS) %>%
mutate(zREB = (REB - mREB)/sdREB) %>%
select(zFG_PCT, zFTA, zPTS, zREB, Jogador, Rookie) %>%
rename(FG_PCT=zFG_PCT, FTA=zFTA, PTS=zPTS, REB=zREB)
vis_pf = zscore_pf %>%
pivot_longer(., cols = c(FG_PCT, FTA, PTS, REB), names_to = 'Var', values_to = 'stats') %>%
ggplot(aes(x = Var, y = stats, color = Rookie, label = Jogador)) +
geom_quasirandom() +
labs(
x = "",
y = "",
title = "Ala-Pivô",
colour = "Experiência"
)
ggplotly(vis_pf, tooltip = "label")
data_pg = read_csv(here::here('./data/data_pg.csv'), show_col_types = FALSE)
glimpse(data_pg)
## Rows: 17
## Columns: 7
## $ MIN <dbl> 32.850, 34.662, 31.336, 31.705, 34.504, 32.780, 31.762, 29.482…
## $ FG_PCT <dbl> 0.438, 0.579, 0.392, 0.467, 0.433, 0.376, 0.459, 0.463, 0.470,…
## $ AST <dbl> 7.271, 7.831, 6.300, 7.106, 9.382, 6.492, 7.331, 2.643, 3.632,…
## $ STL <dbl> 1.045, 1.699, 1.427, 1.568, 0.959, 0.932, 0.885, 0.929, 1.016,…
## $ TOV <dbl> 3.812, 3.485, 2.691, 2.917, 4.472, 2.246, 3.254, 1.429, 2.600,…
## $ Jogador <chr> "Luka Doncic", "Ben Simmons", "Lonzo Ball", "De'Aaron Fox", "T…
## $ Rookie <chr> "Novato", "Novato", "Novato", "Novato", "Novato", "Novato", "N…
data_pg_sd = data_pg %>%
mutate(sdFG_PCT = sd(data_pg$FG_PCT)) %>%
mutate(sdAST = sd(data_pg$AST)) %>%
mutate(sdSTL = sd(data_pg$STL)) %>%
mutate(sdTOV = sd(data_pg$TOV)) %>%
mutate(mFG_PCT = mean(data_pg$FG_PCT)) %>%
mutate(mAST = mean(data_pg$AST)) %>%
mutate(mSTL = mean(data_pg$STL)) %>%
mutate(mTOV = mean(data_pg$TOV))
zscore_pg = data_pg_sd %>%
rowwise() %>%
mutate(zFG_PCT = (FG_PCT - mFG_PCT)/sdFG_PCT) %>%
mutate(zAST = (AST - mAST)/sdAST) %>%
mutate(zSTL = (STL - mSTL)/sdSTL) %>%
mutate(zTOV = (TOV - mTOV)/sdTOV) %>%
select(zFG_PCT, zAST, zSTL, zTOV, Jogador, Rookie) %>%
rename(FG_PCT=zFG_PCT, AST=zAST, STL=zSTL, TOV=zTOV)
vis_pg = zscore_pg %>%
pivot_longer(., cols = c(FG_PCT, AST, STL, TOV), names_to = 'Var', values_to = 'stats') %>%
ggplot(aes(x = Var, y = stats, color = Rookie, label = Jogador)) +
geom_quasirandom() +
labs(
x = "",
y = "",
title = "Armador",
colour = "Experiência"
)
ggplotly(vis_pg, tooltip = "label")
data_sf = read_csv(here::here('./data/data_sf.csv'), show_col_types = FALSE)
glimpse(data_sf)
## Rows: 12
## Columns: 7
## $ MIN <dbl> 25.206, 11.838, 30.872, 32.552, 30.008, 31.426, 40.937, 39.282…
## $ FG_PCT <dbl> 0.456, 0.360, 0.423, 0.445, 0.453, 0.532, 0.440, 0.475, 0.512,…
## $ FTA <dbl> 1.206, 0.618, 1.950, 3.731, 1.863, 2.197, 6.893, 8.744, 2.032,…
## $ FT_PCT <dbl> 0.284, 0.206, 0.465, 0.651, 0.564, 0.584, 0.725, 0.874, 0.531,…
## $ PTS <dbl> 8.838, 3.618, 12.121, 19.241, 12.847, 19.049, 24.082, 27.840, …
## $ Jogador <chr> "OG Anunoby", "Rodions Kurucs", "Cedi Osman", "Jayson Tatum", …
## $ Rookie <chr> "Novato", "Novato", "Novato", "Novato", "Novato", "Novato", "V…
data_sf_sd = data_sf %>%
mutate(sdFG_PCT = sd(data_sf$FG_PCT)) %>%
mutate(sdFTA = sd(data_sf$FTA)) %>%
mutate(sdFT_PCT = sd(data_sf$FT_PCT)) %>%
mutate(sdPTS = sd(data_sf$PTS)) %>%
mutate(mFG_PCT = mean(data_sf$FG_PCT)) %>%
mutate(mFTA = mean(data_sf$FTA)) %>%
mutate(mFT_PCT = mean(data_sf$FT_PCT)) %>%
mutate(mPTS = mean(data_sf$PTS))
zscore_sf = data_sf_sd %>%
rowwise() %>%
mutate(zFG_PCT = (FG_PCT - mFG_PCT)/sdFG_PCT) %>%
mutate(zFTA = (FTA - mFTA)/sdFTA) %>%
mutate(zFT_PCT = (FT_PCT - mFT_PCT)/sdFT_PCT) %>%
mutate(zPTS = (PTS - mPTS)/sdPTS) %>%
select(zFG_PCT, zFTA, zFT_PCT, zPTS, Jogador, Rookie) %>%
rename(FG_PCT=zFG_PCT, FTA=zFTA, FT_PCT=zFT_PCT, PTS=zPTS)
vis_sf = zscore_sf %>%
pivot_longer(., cols = c(FG_PCT, FTA, FT_PCT, PTS), names_to = 'Var', values_to = 'stats') %>%
ggplot(aes(x = Var, y = stats, color = Rookie, label = Jogador)) +
geom_quasirandom() +
labs(
x = "",
y = "",
title = "Ala",
colour = "Experiência"
)
ggplotly(vis_sf, tooltip = "label")
data_sg = read_csv(here::here('./data/data_sg.csv'), show_col_types = FALSE)
glimpse(data_sg)
## Rows: 15
## Columns: 6
## $ MIN <dbl> 28.366, 34.390, 22.661, 33.571, 21.935, 34.875, 30.204, 29.769…
## $ FG_PCT <dbl> 0.430, 0.487, 0.400, 0.430, 0.433, 0.441, 0.425, 0.381, 0.455,…
## $ FG3_PCT <dbl> 0.363, 0.357, 0.222, 0.338, 0.323, 0.409, 0.343, 0.331, 0.376,…
## $ PTS <dbl> 14.611, 20.581, 7.017, 22.083, 11.043, 17.556, 15.093, 14.019,…
## $ Jogador <chr> "Bogdan Bogdanovic", "Shai Gilgeous-Alexander", "Josh Okogie",…
## $ Rookie <chr> "Novato", "Novato", "Novato", "Novato", "Novato", "Novato", "N…
data_sg_sd = data_sg %>%
mutate(sdFG_PCT = sd(data_sg$FG_PCT)) %>%
mutate(sdFG3_PCT = sd(data_sg$FG3_PCT)) %>%
mutate(sdPTS = sd(data_sg$PTS)) %>%
mutate(mFG_PCT = mean(data_sg$FG_PCT)) %>%
mutate(mFG3_PCT = mean(data_sg$FG3_PCT)) %>%
mutate(mPTS = mean(data_sg$PTS))
zscore_sg = data_sg_sd %>%
rowwise() %>%
mutate(zFG_PCT = (FG_PCT - mFG_PCT)/sdFG_PCT) %>%
mutate(zFG3_PCT = (FG3_PCT - mFG3_PCT)/sdFG3_PCT) %>%
mutate(zPTS = (PTS - mPTS)/sdPTS) %>%
select(zFG_PCT, zFG3_PCT, zPTS, Jogador, Rookie) %>%
rename(FG_PCT=zFG_PCT, FG3_PCT=zFG3_PCT, PTS=zPTS)
vis_sg = zscore_sg %>%
pivot_longer(., cols = c(FG_PCT, FG3_PCT, PTS), names_to = 'Var', values_to = 'stats') %>%
ggplot(aes(x = Var, y = stats, color = Rookie, label = Jogador)) +
geom_quasirandom() +
labs(
x = "",
y = "",
title = "Ala-Armador",
colour = "Experiência"
)
ggplotly(vis_sg, tooltip = "label")